Forthcoming and Online First Articles

International Journal of Hydromechatronics

International Journal of Hydromechatronics (IJHM)

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International Journal of Hydromechatronics (4 papers in press)

Regular Issues

  • Robust active disturbance rejection control for modular fluidic soft actuators   Order a copy of this article
    by Yunce Zhang, Tao Wang, Xuqu Hu 
    Abstract: Delicate dynamic control of soft actuators is a challenging task due to their strongly nonlinearities. This article focuses on the dynamic control of the modular fluidic soft actuators governed by pneumatic proportional valves. Since it is difficult to accurately describe the complex coupling relationships among the chambers of the soft actuators, the dynamic control of the soft actuators cannot be implemented by using advanced control algorithms based on precise model in usual. To improve the manipulability and extend the application scenarios, we design a robust active disturbance rejection control method based on linear extended state observer, which only requires an approximate model of the soft actuators. Experimental results show that closed-loop stability and good tracking performance are achieved by the proposed method, meanwhile better disturbance rejection ability is guaranteed in comparison to the commonly used proportional-integral-differential control method.
    Keywords: controller design; state observation; dynamic performance; fluidic power; modular soft actuators.
    DOI: 10.1504/IJHM.2023.10059853
     
  • Research on electro-mechanical actuator fault diagnosis based on ensemble learning method   Order a copy of this article
    by Jianxin Zhang, Muyang Liu, Wenzhu Deng, Zhen Zhang, Xiaowang Jiang, Geng Liu 
    Abstract: With the rapid development of the aviation industry, people have increasingly higher requirements for the performance of aircraft. Therefore, effective health management of the airborne electro-mechanical actuator (EMA) is particularly critical. Aiming at the problem of aircraft health management, this paper first establishes the simulation model of EMA, and chooses the three-phase current as the characteristic quantity of subsequent fault diagnosis through the analysis of the model. Then an EMAs fault diagnosis framework based on ensemble learning method is proposed. The study compares the advantages and disadvantages of different ensemble learning strategies and proposes a fault diagnosis framework based on the Boosting ensemble learning method, which is based on XGBoost, LightGBM, and CatBoost models. Compared with popular deep learning frameworks (CNN), this method requires fewer computing resources and has stronger interpretability of the model. The test results indicate that the proposed framework has higher diagnosis accuracy compared to traditional machine learning methods and shorter training time and lower memory usage compared to deep learning methods (CNN), making it a valuable tool for engineering applications.
    Keywords: electro-mechanical actuator; EMA; permanent magnet synchronous motor; health management; fault diagnosis; ensemble learning.
    DOI: 10.1504/IJHM.2023.10060525
     
  • Determination of the flow rate characteristics of porous media under the positive pressure and vacuum   Order a copy of this article
    by Wei Zhong, Yihao Wang, Kaiwen Fu, Chong Li, Jiang Shao, Pengfei Qian 
    Abstract: Porous media is widely used to replace the conventional orifices as restrictors in vacuum handling process. In this study, a theoretical model describing the flow rate characteristics, including effects from both viscosity and inertia, is established based on Darcy-Forchheimer’s law. The simulation work is firstly conducted, followed by establishing apparatuses to determine permeability and inertial coefficients. The permeability is determined within a small pressure difference (< 2 kPa) and the inertial coefficient is obtained with Re > 0.1 as the boundary. The average permeability is 1.21 × 10^-12 m² , 1.56 × 10^-12 m² , 3.41 × 10^-12 m² and 12.21 × 10^-12 m² , respectively. The inertial coefficient is determined under the positive pressure at the maximum pressure difference and vacuum with pressure difference from 50 kPa to 70 kPa. For different pressure conditions, it is confirmed that the theoretical flow rate can predict the experimental data within a 3% uncertainty which is sufficient for most applications. Finally, to obtain the inertial coefficient, two methods including the single-point method and the multi-point method are proposed. We found that the single-point method gives an error of 3.1% while the multi-point method gives an error of 1.9% for the determination of the entire flow rate characteristics.
    Keywords: flow rate characteristics; porous media; positive pressure; vacuum; permeability; inertial coefficient.
    DOI: 10.1504/IJHM.2024.10062649
     
  • Artificial intelligence-based viscosity prediction of polyalphaolefin-boron nitride nanofluids   Order a copy of this article
    by Omer A. Alawi, Haslinda Mohamed Kamar, Mustafa Mudhafar Shawkat, Mohammed M. Al-Ani, Hussein A. Mohammed, Raad Z. Homod, Mazlan A. Wahid 
    Abstract: Predicting viscosity’s nanofluids can benefit all domains, including energy, thermofluids, power systems, energy storage, materials, cooling, heating, and lubrication. The objective of this study to predict the dynamic viscosity of polyalphaolefin-hexagonal boron nitride (PAO/hBN) nanofluids using four main parameters: shear rate, shear stress, nanomaterials mass fraction, and temperature. Moreover, three hybrid ensemble learning models (Bayesian ridge-random forest, Bayesian ridge-MLP regressor and Bayesian ridge-AdaBoost regressor) were developed for the current task. The forward sequential feature selector (FSFS) created four input combinations (models). Model 4 showed the best prediction accuracy, followed by models 2, 3 and 1. The computational findings showed that ensemble learner 1 was slightly outperformed ensemble learner 3. Meanwhile, among the predictive models, ensemble learner 2 consistently placed third. Besides, the research results demonstrated that creating predictive models based on all input parameters can produce a precise prediction matrix. Overall, the study recommended exciting conclusions on predicting a nanolubricant’s viscosity for use in heat transfer applicants.
    Keywords: nanofluids; viscosity; polyalphaolefin; PAO; machine learning; ensemble learning; boron nitride.
    DOI: 10.1504/IJHM.2024.10063148